NVIDIA Quadro RTX 8000 GPU Review

alex_stief

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May 31, 2016
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Maybe it's just me, but the closing statement about the "psychological" reason to buy the highest end product possible sounds weird to me.
It brings back memories of the "just buy it" article about RTX cards published on a different site.
Call me stingy, but I would rather base hardware purchases on actual requirements. Not every CAD engineer needs the performance and memory size of this top-end GPU. I work with CAD from time to time, and I would shake my head if my company decided to go all-in on expensive hardware, in an attempt to motivate me.
Don't get me wrong, I see the appeal of buying powerful hardware for qualified workers. But more than the price, I like being asked about my actual requirements.
 
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Cixelyn

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Nov 7, 2018
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Not sure about CAD, but with deep learning workstations, I think the above "psychological" reason definitely applies.

Model sizes have been constantly getting larger and larger for the past few yeras.
It used to be that random pytorch & tensorflow repos would work with 4GB, now the minimum seems to be 8 or even 16GB for a single card. Today's perceived requirements might be obsolete within the span of a few months.

For a research workstation, I'd definitely err on the side of adding more VRAM, as the workarounds for not having enough are rather complex.
 

alex_stief

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Those are real requirements. Not buying expensive for the sake of expensiveness, as the article suggests.
 

Patrick

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Dec 21, 2010
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Maybe it's just me, but the closing statement about the "psychological" reason to buy the highest end product possible sounds weird to me.
Just buy it is famous because no testing went into the statement. It was a blind recommendation on a site that used to be famous for data driven recommendations. That is why it is a famous line and very different than pointing out a psychological reason people purchase these cards after doing a review.

I can tell you, I have spoken to a dozen local companies in the valley that will not buy their DL/ AI engineers less than Titan RTX's for workstations. There is such a big shortage for top talent, that 0-2 years out of school there are folks, that are great at computer vision as an example, making $400K+ in addition to equity-based comp. If those folks do not like the job, they just leave and go to another company that will pay them the same or more.

In that context, $3000 or so per GPU to minimize frustrations and keep one person from jumping to a competitor or another company is huge. I have heard it now on numerous occasions.

To me, the funnier part is that most of those same companies have large clusters. It is still effectively an HR move to keep people and attract others. These guys go to bars and tell everyone their company buys them Titan RTX/ Tesla V100/ DGX Stations/ Quadro RTX and those who have RTX 2080's become interested in joining a place with better hardware. If you have a 48GB card, and can run models in-memory that others at the bar cannot, people think your company is helping the data science. Keeping one person retained/ or generating a new hire lead pays for everyone's GPUs.
 

William

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May 7, 2015
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I think STH needs Titan RTX/RTX8000's in all reviewer workstations... yup that sounds good LOL :)
 
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AndrewH

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May 9, 2019
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Would you be able to provide a higher resolution version of the third image in the review (the back of the card's PCB)? I'm interested in what memory chips are being used and it's difficult to make out the text on them in the photo on the article.
 

gigatexal

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Nov 25, 2012
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Just buy it is famous because no testing went into the statement. It was a blind recommendation on a site that used to be famous for data driven recommendations. That is why it is a famous line and very different than pointing out a psychological reason people purchase these cards after doing a review.

I can tell you, I have spoken to a dozen local companies in the valley that will not buy their DL/ AI engineers less than Titan RTX's for workstations. There is such a big shortage for top talent, that 0-2 years out of school there are folks, that are great at computer vision as an example, making $400K+ in addition to equity-based comp. If those folks do not like the job, they just leave and go to another company that will pay them the same or more.

In that context, $3000 or so per GPU to minimize frustrations and keep one person from jumping to a competitor or another company is huge. I have heard it now on numerous occasions.

To me, the funnier part is that most of those same companies have large clusters. It is still effectively an HR move to keep people and attract others. These guys go to bars and tell everyone their company buys them Titan RTX/ Tesla V100/ DGX Stations/ Quadro RTX and those who have RTX 2080's become interested in joining a place with better hardware. If you have a 48GB card, and can run models in-memory that others at the bar cannot, people think your company is helping the data science. Keeping one person retained/ or generating a new hire lead pays for everyone's GPUs.
wait wait wait you can make almost half a mil doing computer vision? I am in the wrong field for sure
 
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